Many applications, including the handling of pharmaceutical pills and capsules, include a counting of objects. Assessments of object counting techniques focus on various aspects, including accuracy, speed, cost, and reliability. Object counting methods can be placed into three broad categories: (1) manual methods; (2) semi-automated methods; and (3) automated methods.
The manual methods include, for example, taking a quantity of the desired objects from a bulk supply container, placing them onto a surface, and separating the desired number of objects using a combination of human manual skills and human perception. This counting process can include counting the objects individually or counting multiples of a single object. When used extensively, this manual counting approach is monotonous and stressful, thereby resulting in human error and inaccurate counts. Such inaccuracies are particularly undesirable when large numbers of costly objects (e.g., pharmaceutical pills) are being counted, and may have costly legal implications.
The semi-automated methods rely on counting the objects in larger quantities, such as pre-packaged dozens or as a fully, or partially, filled tray where the tray includes an array of bins. The savings in time resulting from pre-packaging the larger quantities is countered by the cost of packaging and possible errors associated with the packaging process itself. Furthermore, the need to count quantities that are not a multiple of the pre-packaged amount (e.g., a dozen) adds human involvement and hence results in human counting error. Trays possessing bin arrays are available in the marketplace. They can be efficient, low-cost and free of counting errors, if the desired count is a multiple of the full capacity of the tray, provided that only a single object occupies each bin in the tray. The conditions of counting using a full tray and a single object in each bin are not satisfied all the time. Thus, human involvement and the resulting counting errors are again introduced.
The automatic counting methods can be subdivided into the following groups:                (1) “Feed and Sense” methods, where the objects are fed, one-at-a-time, past one or more sensors.        (2) “Global” methods, where a priori knowledge of a measurable property or attribute of an individual object is compared to the cumulative or “global” measured attribute of a few similar objects.        (3) Feature, or attribute, based inspection and identification by comparison to a database of features or attributes.        
The “feed and sense” counting techniques are typically configured using one of a variety of combinations of object feeding methods and object sensing methods. Object feeding, for example, can be accomplished by gravity, mechanical vibration, belt transport, air stream, and friction force. On the other hand, examples of object sensing, or counting methods, include: (1) rotating gates whose rotation shaft is attached to a mechanical counter; (2) an optical source and its matched photo-detector pair producing different detector output signals by the presence or absence of an object between the source and the detector; (3) an on/off electromechanical switch triggering a voltage signal when an object passes by; and (4) a proximity switch detecting the presence or absence of metallic objects passing by the switch.
Many of the existing automatic dispensing systems rely on “feed and sense” counting systems, which are configured with one of the combinations of feeding and sensing methods described above. In order to ensure accurate counting, most of these systems rely on feeding the objects past a sensor, one-at-a-time, often using sophisticated mechanisms and configuration geometries. Feeding objects one-at-a-time is the most challenging part of building a counter. The lack of accurate counting is usually a result of the failure to reliably feed single objects past the sensing element. Furthermore, broken objects and foreign objects add another challenge to these counting methods. A broken object, for example, will be counted by these systems as two objects, unless size information is made available to aid in the decision to count the broken parts or ignore them. Also, a foreign object, mixed with the desired objects, can accidentally pass through the counter and be mistakenly included in the count. These challenges can be reduced using human inspection, but again human involvement may result in counting inaccuracies. In some existing counter design cases, the user is required to almost feed the objects one-at-a-time out of its bulk supply container into the counter, in order to insure accurate counting, thus defeating the purpose of having a counter.
The “global” automatic counting approach is based on a priori knowledge of a measurable property or attribute of an individual object. By measuring the cumulative or “global” attribute of “N” similar objects one can find the number of objects, N, in the group. Clearly, this requires that a measure of the attribute of a group of “N” similar objects is the superposition of the measure of the attribute of a single object, N-times. Weighing is one example of a global counting approach, where the total weight of a number of objects is compared to the weight of a single object, in order to find the number of objects in the group. Another example is the measure of area of a group of similar objects, obtained by an analog sensor, which is viewed as a superposition of the measure of the known area of a single object. This superposition principle is flawed because of the object-to-object variability due to manufacturing and the potential non-linearity in the sensor measuring the attribute, as well as the possible distorting effects due to the optics involved. Further, it has been found that, counter-intuitively, measuring the projection area attribute at higher resolution does not ensure the applicability of the superposition principle when using digital images of the projected area of a single object and a group of objects. Using pixel counting, where each pixel is a few micrometers in extent, the superposition did not apply all the time, as the size of the object was changed over a range of values, from small to large.
The feature/attribute identification-based automatic counting approach involves the inspection and search for a characteristic such as color, pattern, shape, etc., or a combination of these characteristics. The identification of the object is accomplished by a comparison to a database of characteristics. Furthermore, measuring the color of an object adds complexity and costs to the counting system, since more sophisticated standards are required for the illumination sources, the geometry of illumination and collection of the light reflected from the object. Also, a color image takes three times the storage memory required for a black and white image, and the processing time to determine the count is expected to be longer. Typically, the light sources used in color measurements are required to operate at a high color temperature, which generates heat. Heat needs to be vented by fans and shortens the life of the light source. As the source intensity declines, over time, the parameters of the color being measured will shift, thus causing loss of object identity and hence counting inaccuracies will occur.
As described above, object counting accomplished by manual, semi-automated, and automated counting techniques produce counting inaccuracies. These inaccuracies may result from, for instance, human error, multiple object feeding, broken object feeding, inclusion of foreign objects, or the inapplicability of the superposition principle. Compared to manual methods, automatic counting methods typically shorten the counting time by different degrees; however, counting errors are not eliminated. Manual or semi-automated (i.e., semi-manual) methods cause fatigue and stress and still leave the issue of human error unresolved. Furthermore, the “feed and sense” automatic methods often require maintenance of certain parts of the system to ensure efficient sensing and to prevent object cross contamination. Also, the “global” methods provide counting speed but suffer from inaccuracy.
Based on the foregoing, a need still exists for an improved object counting and identification verification system.